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Python for Data Analysis
Course
Python for Data Analysis
Get started with Python and build essential skills for data analysis in just 5 weeks—no prior programming experience is required.
Flexible Schedule
Intermediate Level
Mentor Support
Estimated Time
15 hours
Python is the most popular programming language used in data science (Statista). So, if youre keen to kickstart your career in data analytics, get ready to dive into the world of data with Python! This course is your ticket to mastering data analysis and building data models using Python. Its the perfect introduction to this in-demand language for aspiring data scientists and analysts.
During the course, youll learn to import data from various sources and explore ways to clean and prepare it for exploratory data analysis (EDA) and eye-catching visualizations. You'll also predict future trends by creating linear, multiple, and polynomial regression models and pipelines and understand how to evaluate them like a pro.
As you progress, youll build your practical understanding of Python through hands-on labs where you'll explore open-source Python libraries like Pandas and NumPy for data manipulation and visualization. Plus, you'll gain proficiency in using SciPy and scikit-learn to build machine-learning models and make predictions.
Enroll today and build job-ready skills in one of the worlds most popular programming languages.
This course comprises six purposely designed modules that take you on a carefully defined learning journey.
It is a self-paced course, which means it is not run to a fixed schedule with regard to completing modules. It is anticipated that you will complete the course in 15 hours. However, as long as the course is completed by the end of your enrollment, you can work at your own pace. And dont worry, youre not alone! You will be encouraged to stay connected with your learning community through the course discussion space.
The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include discussion space, videos, reading material, quizzes, hands-on labs, quizzes and final assignment.
Once you have successfully completed the course, you will earn your IBM Certificate.
You will be able to:
- Import, clean, and prepare data for analysis.
- Use Pandas, DataFrames, NumPy, and SciPy.
- Load, manipulate, analyze, and visualize data.
- Build machine learning models.
- Beginners to data analysis
- Aspiring data analysts and data scientists
- Career changers or students entering data-related fields
- No prior experience is necessary.
Course Outline
General Information
Learning Objectives
Syllabus
Grading Scheme
Module Introduction & Learning Objectives
Video: Course Introduction (1:44)
Video: Understanding the Data (2:36)
Video: Python Packages for Data Science (2:36)
Video: Importing and Exporting Data in Python (4:19)
Video: Getting Started Analyzing Data in Python (4:29)
Video: Accessing Databases with Python (4:07)
Reading: Lesson Summarys
Practice Quiz: Importing Data Sets
Lab: Importing Data Sets
Graded Quiz: Importing Data Sets (5 Questions)
Module Introduction & Learning Objectives
Video: Pre-processing Data in Python (2:18)
Video: Dealing with Missing Values in Python (6:06)
Video: Data Formatting in Python (3:32)
Video: Data Normalization in Python (3:44)
Video: Binning in Python (1:57)
Video: Turning Categorical Variables into Quantitative Variables in Python (2:11)
Lab: Data Wrangling - Used Cars Pricing
Lab: Data Wrangling - Laptop Pricing
Reading: Lesson Summary
Practice Quiz: Data Wrangling
Graded Quiz: Data Wrangling (5 Questions)
Module Introduction & Learning Objectives
Video: Exploratory Data Analysis (1:28)
Video: Descriptive Statistics (4:47)
Video: GroupBy in Python (3:29)
Video: Correlation (2:39)
Video: Correlation - Statistics (2:45)
Lab: Exploratory Data Analysis - Laptop Pricing
Lab: Exploratory Data Analysis - Used Car Pricing
Reading: Lesson Summarys
Practice Quiz: Exploratory Data Analysis
Graded Quiz: Exploratory Data Analysis (5 Questions)
Module Introduction & Learning Objectives
Video: Model Development (1:52)
Video: Linear Regression and Multiple Linear Regression (6:42)
Video: Model Evaluation using Visualization (4:52)
Reading: Kernel Density Estimation (KDE) Plots for Model Evaluation
Video: Polynomial Regression and Pipelines (4:34)
Video: Measures for In-Sample Evaluation (4:45)
Video: Prediction and Decision Making (5:12)
Reading: Lesson Summarys
Practice Quiz: Model Development
Lab: Model Development - Used Car Pricing
Lab: Model Development - Laptop Pricing
Graded Quiz: Model Development (7 Questions)
Module Introduction & Learning Objectives
Video: Model Evaluation and Refinement (7:39)
Video: Overfitting, Underfitting and Model Selection (4:28)
Reading: Ridge Regression Introduction
Video: Ridge Regression (4:37)
Video: Grid Search (4:40)
Reading: Lesson Summarys
Practice Quiz: Model Evaluation and Refinement
Lab: Model Evaluation and Refinement - Used Cars Pricing
Lab: Model Evaluation and Refinement - Laptop Pricing
Graded Quiz: Model Evaluation and Refinement (5 Questions)
Module Introduction & Learning Objectives
Reading: Project Scenario
Lab for Final Project - Data Analytics for House Pricing Data Set
Final Assignment
Cheat Sheet: Data Analysis for Python
Final Exam Instructions
Final Exam (20 Questions)
Why Learn with SkillUp Online?
We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.
Reskilling into tech? We’ll support you.
Upskilling for promotion? We’ll help you.
Cross-skilling for your career? We’ll guide you.
Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
Designed by the industry for the industry so you can build job-ready skills.
Job-Ready Skills Focus
Competency building and global certifications employers are actively looking for.
FAQs
If you want to start your journey in data analytics, this Python for Data Analysis course is one of the best beginner-friendly options. It combines theory with hands-on labs, guiding you through importing, cleaning, preparing, and analyzing data while also introducing you to predictive modeling.
Yes, Python is one of the most widely used tools in data science and analytics. With libraries like Pandas, NumPy, SciPy, and scikit-learn, Python makes tasks such as data cleaning, data visualization, and building machine learning models highly efficient. This Python Data Analysis course helps you gain these skills step by step.
You can get familiar with Python basics in a week, but becoming proficient in data analysis takes more practice. This 5-week Python Data Analysis course is structured to gradually build your skills in data preparation, exploratory data analysis (EDA), and model development.
The five steps are:
- Collect and import data
- Clean and prepare data
- Explore and summarize data
- Build and refine models
- Evaluate and visualize results
Each of these steps is covered in this Data Analysis with Python course.
The demand for data analysts skilled in Python continues to grow across industries. With its robust ecosystem of libraries, Python is expected to remain the leading language for data analysis, machine learning, and AI applications.
In India, entry-level data analysts earn an average of 47 LPA, while experienced professionals can earn significantly more. In the US, salaries typically range from $60,000 to over $90,000 annually, depending on skills and experience.
To succeed as a data analyst, you need to master the fundamentals of Python, data structures, and key libraries like Pandas, NumPy, and scikit-learn. This Python Data Analysis course ensures you gain these essentials through hands-on labs and projects.
The best starting point is a structured program like this Python for Data Analysis course. Youll cover the core skills step by step, including importing data, cleaning and preparing it, exploring datasets, and building predictive models.
This course is designed for beginners who want to start a career in data analytics or data science. No prior programming knowledge is required.
No. The Python for Data Analysis course is beginner-friendly and requires only basic computer skills. Youll learn coding from scratch with guided exercises.
The course can be completed in 5 weeks with a few hours of learning each week.
Completing this Python for Data Analysis course prepares you for entry-level roles like data analyst, junior data scientist, or business intelligence associate. Youll also have a strong foundation for moving into machine learning and AI.
Yes, you will receive an IBM Certificate upon successful completion. This certificate highlights your skills in data analysis with Python and can be shared with employers or on platforms like LinkedIn.
Type of certificate
IBM Certificate
About this course
06 Modules
05 Skills
Includes
Discussion space
09 Hands-on labs
05 Practice quizzes
05 Graded quizzes
01 Final exam
01 Final project
Create
Exploratory Data Analysis
Model Development
Exercises to explore
Importing Data Sets
Data Wrangling
Model Evaluation and Refinement
This course has been created by
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